Articles | Volume 30, issue 3
https://doi.org/10.5194/npg-30-263-2023
https://doi.org/10.5194/npg-30-263-2023
Research article
 | 
06 Jul 2023
Research article |  | 06 Jul 2023

An adjoint-free algorithm for conditional nonlinear optimal perturbations (CNOPs) via sampling

Bin Shi and Guodong Sun

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Cited articles

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Short summary
We introduce a sample-based algorithm to obtain the conditional nonlinear optimal perturbations. Compared with the classical adjoint-based method, it is easier to implement and reduces the required storage for the basic state. When we reduce the number of samples to some extent, it reduces the computation markedly more when using the sample-based method, which can guarantee that the CNOP obtained is nearly consistent with some minor fluctuating errors oscillating in spatial distribution.